{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:47:33Z","timestamp":1769636853642,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,18]],"date-time":"2021-04-18T00:00:00Z","timestamp":1618704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.<\/jats:p>","DOI":"10.3390\/s21082855","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T21:59:49Z","timestamp":1618869589000},"page":"2855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Breast Mass Detection in Mammography Based on Image Template Matching and CNN"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7369-5494","authenticated-orcid":false,"given":"Lilei","family":"Sun","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huijie","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang 330044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School of Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21654","article-title":"Cancer Statistics, 2021","volume":"71","author":"Siegel","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1093\/jjco\/hyu073","article-title":"Five-year relative survival rate of breast cancer in the USA, Europe and Japan","volume":"44","author":"Katanoda","year":"2014","journal-title":"Jpn. J. Clin. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s00521-015-2103-9","article-title":"Breast cancer diagnosis using GA feature selection and Rotation Forest","volume":"28","author":"Subasi","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"012033","DOI":"10.1088\/1757-899X\/495\/1\/012033","article-title":"Machine Learning Classification Techniques for Breast Cancer Diagnosis","volume":"Volume 495","author":"Omondiagbe","year":"2019","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., and Joskowicz, L. (2020). MommiNet: Mammographic Multi-View Mass Identification Networks. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-59716-0"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.engappai.2006.05.010","article-title":"A SVM-based approach to microwave breast cancer detection","volume":"19","author":"Kerhet","year":"2006","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.compbiomed.2005.12.004","article-title":"Automated detection of masses in mammograms by local adaptive thresholding","volume":"37","author":"Kom","year":"2007","journal-title":"Comput. Biol. Med."},{"key":"ref_8","unstructured":"Xu, X., Xu, S., Jin, L., and Zhang, S. (2010, January 23\u201326). Using PSO to improve dynamic programming based algorithm for breast mass segmentation. Proceedings of the 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), Changsha, China."},{"key":"ref_9","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_10","unstructured":"Kuo, Y.C., Lin, W.C., Hsu, S.C., and Cheng, A.C. (2014, January 10\u201312). Mass detection in digital mammograms system based on PSO algorithm. Proceedings of the 2014 International Symposium on Computer, Consumer and Control, Taichung, Taiwan."},{"key":"ref_11","unstructured":"StojiC, T., Reljin, I., and Reljin, B. (2005, January 14\u201315). Local contrast enhancement in digital mammography by using mathematical morphology. Proceedings of the International Symposium on Signals, Circuits and Systems, 2005 ISSCS 2005, Iasi, Romania."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Amutha, S., Babu, D.R., Shankar, M.R., and Kumar, N.H. (2011, January 9\u201311). Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet. Proceedings of the 2011 IEEE International Symposium on IT in Medicine and Education, Guangzhou, China.","DOI":"10.1109\/ITiME.2011.6130897"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, F., Zhang, F., Gong, Z., Chen, Y., and Chai, W. (2012, January 16\u201318). A fully automated scheme for mass detection and segmentation in mammograms. Proceedings of the 2012 5th International Conference on BioMedical Engineering and Informatics, Chongqing, China.","DOI":"10.1109\/BMEI.2012.6513093"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TCYB.2020.2987164","article-title":"Generalized incomplete multiview clustering with flexible locality structure diffusion","volume":"51","author":"Wen","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., and Joskowicz, L. (2020). Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I, Springer Nature.","DOI":"10.1007\/978-3-030-59716-0"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wo\u017aniak, M., Si\u0142ka, J., and Wieczorek, M. (2021). Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput. Appl., 1\u201316.","DOI":"10.1007\/s00521-021-05841-x"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.eswa.2019.01.060","article-title":"A neuro-heuristic approach for recognition of lung diseases from X-ray images","volume":"126","author":"Ke","year":"2019","journal-title":"Expert. Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wen, J., Zhang, Z., Xu, Y., Zhang, B., Fei, L., and Xie, G.S. (2020). CDIMC-Net: Cognitive Deep Incomplete Multiview Clustering Network. International Joint Conference on Artificial Intelligence, AAAI Press.","DOI":"10.24963\/ijcai.2020\/447"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cmpb.2018.01.017","article-title":"Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system","volume":"157","author":"Almasni","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_22","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_23","unstructured":"Heath, M., Bowyer, K., Kopans, D., Moore, R., and Kegelmeyer, W.P. The Digital Database for Screening Mammography. Proceedings of the 5th International Workshop on Digital Mammography."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","article-title":"Deep Neural Networks Improve Radiologists\u2019 Performance in Breast Cancer Screening","volume":"39","author":"Wu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TMI.2007.895460","article-title":"A Concentric Morphology Model for the Detection of Masses in Mammography","volume":"26","author":"Eltonsy","year":"2007","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nagi, J., Kareem, S.A., Nagi, F., and Ahmed, S.K. (December, January 30). Automated breast profile segmentation for ROI detection using digital mammograms. Proceedings of the 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2010.5742205"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s10278-016-9923-8","article-title":"Microcalcification segmentation from mammograms: A morphological approach","volume":"30","author":"Ciecholewski","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1118\/1.1589494","article-title":"Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information","volume":"30","author":"Tourassi","year":"2003","journal-title":"Med. Phys."},{"key":"ref_30","unstructured":"Cover, T.M., and Allen, T. (1991). Elements of Information Theory, Wiley Series in Telecommunications, Tsinghua University Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00452-8","article-title":"Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector","volume":"2","author":"Divyashree","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_32","unstructured":"Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., and Muller, K.E. (1990, January 22\u201325). Contrast-limited adaptive histogram equalization: Speed and effectiveness. Proceedings of the First Conference on Visualization in Biomedical Computing, IEEE Computer Society, Atlanta, GA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9493","DOI":"10.1007\/s11042-020-09991-3","article-title":"Automatic computer-aided diagnosis system for mass detection and classification in mammography","volume":"80","author":"Lbachir","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_36","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1118\/1.2890080","article-title":"A model-based framework for the detection of spiculated masses on mammography","volume":"35","author":"Sampat","year":"2008","journal-title":"Med. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, Y., Shi, W., Cui, L., Wang, H., Bu, Q., and Feng, J. (2018). Automatic Mass Detection from Mammograms with Region-Based Convolutional Neural Network. Chinese Conference on Image and Graphics Technologies, Springer.","DOI":"10.1007\/978-981-13-1702-6_44"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13005","DOI":"10.1007\/s11042-018-6259-z","article-title":"Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry","volume":"78","author":"Junior","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, F., Zhang, Q., Wang, S., Wang, Y., and Yu, Y. (2020, January 13\u201319). Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00387"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cao, H. (2020). Breast mass detection in digital mammography based on anchor-free architecture. arXiv.","DOI":"10.1016\/j.cmpb.2021.106033"},{"key":"ref_42","first-page":"2999","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., and Savvides, M. (2019, January 15\u201320). Feature selective anchor-free module for single-shot object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","article-title":"FoveaBox: Beyound Anchor-Based Object Detection","volume":"29","author":"Kong","year":"2020","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:30Z","timestamp":1760161770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,18]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082855"],"URL":"https:\/\/doi.org\/10.3390\/s21082855","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,18]]}}}